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I realized recently that I had not used my Amazon Kindle (Kindle 3, or now Kindle Keyboard) in more than a month, despite the fact that I read books every day. In fact, I found it buried under some physical books, battery dead, and then charged it, forgot it again for another month, and discovered it dead again.

As an HCI advocate, avid reader, and someone who initially enjoyed the Kindle a lot, I want to examine some of the reasons I drifted away from it. I think there are five key reasons, which I’ll discuss in turn.

Multiple books & tactile

Notes

Random access

Layout

Content pricing

Multiple books & tactile. I usually read multiple books at a time, and the Kindle doesn’t fit that very well psychologically. True, you can easily navigate from the current book back to the home menu, select a different book, and pick up right where you were reading earlier. But the psychological problem is this: the book will look exactly the same as the one you were just reading. There is no tactile, olfactory, kinesthetic, or any other sense that you have changed your reading material and returned to another place. This reduces the psychological effect of having switched gears, which is a bit of excitement every time I set down one book and pick up another. You also lose the sense of place within a book (the differential weight of pages on each hand, the sense of location within the text, etc.) Thus, the Kindle pulls strongly for reading one book at a time, which I don’t do.

Notes. I often take notes as I read, and the Kindle is not good for that. True, you can select the menu, insert a note, and type it on the miniscule keyboard “ but going back to those notes later is tedious at best, and in fact only once have I reviewed notes that Iv’e taken. In a physical book, there is a sense that marginal scribbling is a creative act in some way. Indeed, I’ve seen scholarly libraries where margin notes were the primary value. On the Kindle, it feels like notes are an afterthought that exist to check off a feature list, not something that actually works well.

Random access. Closely related to the notes issue, there is no good way to skip around in a Kindle book. The physical nature of printed books is extremely helpful for both memory and search. One may recall, for instance, I saw that chart somewhere around the middle and it was at the top of a page, and a quick flip through the pages will reveal it. Likewise, reading often needs a quick flip-back-and-return to locate some fact (now who is Charlotte again?) before continuing. The Kindle is terrible for that. Partially offsetting this is that one can do a text search; but I suspect the need to find a particular word or phrase is less than the need to do quick page-throughs and reviews.

Layout. The Kindle layout is great for fiction and general non-fiction, but not very good for technical materials such as books about statistics and programming. At any given time, I’m usually working my way through a couple of technical books, which I prefer in print. (Besides the layout problem, technical books also pose issues for note-taking and random access, as described above.)

Content pricing. Finally, Kindle has not yet nailed its pricing model. Amazon seems to be apologetic about this, with notes on some titles proclaiming that the price was inflated by the publisher. For my part, I often see things like this: printed technical book is $40 while Kindle is $35. Ouch. In that case, I get print because it adds a lot more value. What I’d really like, however, is a bundle option: Kindle = $35, print = $40, print + Kindle = $50. I need the print edition for all the reasons noted above, but would like to add a Kindle edition for convenience. But that’s not worth an additional 80-100% of the print edition price. I’m guessing that publishers insist on this, and don’t want a bundle model because someone could buy the bundle, sell the print copy, and keep the Kindle copy at a discount. But come on! My guess is that such behavior (a) would be rare; (b) would be more than offset immediately by market gains; and (c) would increase overall readership and loyalty which has downstream bonuses.

Positives. Of course there are many positive aspects of the Kindle. In particular, the form factor is nicely engineered for a compromise between size and comfort; text is crisp and easy to read; the price is amazing; the availability of content is enormous; and the portability of one’s library is delightful. I won’t rehash those in detail, but they are the reason I bought one to begin with, and why I enjoyed it so much for the first couple of months.

Conclusion and recommendation. If you read a lot of fiction or general non-fiction (history, etc.) that proceeds linearly through a text, then it’s a no-brainer: Kindle is great. If, however, you read mostly highly formatted or technical works (statistics, science texts, cookbooks, art, etc.) then Kindle is mediocre to poor. And if you want PDF support, then it’s downright terrible (except perhaps for the DX, which I haven’t tried).

The CFP for the Advanced Research Techniques Forum is now available from the AMA: ART Forum CFP

The ART Forum features groundbreaking work on both breadth and depth issues for advanced analytics and modeling applied to real-world business problems, and many papers go on to be featured in top tier journals. Please consider submitting your work. The due date for submissions is November 30, 2011. Authors will be notified of the outcome in mid-December.

One of my favorite conferences is the Advanced Research Techniques Forum (ART Forum) from the American Marketing Association (disclosure: I’m chairing the conference for 2012). At this year’s conference I was happy to announce that the 2012 conference will be:

June 24-27, 2012 in Seattle!

If you’re a researcher interested in the latest customer/marketing research innovations, please consider submitting your work. The CFP will be out soon, with abstracts due in late Fall. For ideas, check out the 2011 ART Forum program.

We also welcome suggestions for the conference: topics to include, tutorials you would like, or suggestions for speakers or future locations. You can find my contact info on the “about me” page, or on LinkedIn. We hope to see you in June in Seattle!

In Puget Sound, 2011 is turning out to be the coolest year on record for grape growing. Currently, we’re approximately 4 weeks behind normal heat accumulation, and a full week behind 2010 which was terribly cool. According to the WSU site (at Mt Vernon, but the best records for Western Washington), Puget Sound has had 1054 degree days this season (we need 1600 or so to ripen the cool climate varieties). Cf. WSU Washington AVA records.

My own Regent grapes have great-looking clusters, definitely well-rested after the poor showing in 2010. But veraison has not started at all, and with 6 weeks needed between color change and harvest, we’re pushing late October (and rain potential) for harvest.

This has me wondering about one model of what climate change might bring: that heat inland forces air to rise there, which then pulls in more marine layer from the Pacific Ocean. Under that model, places like Napa Valley that have coastal outlets could get cooler and wetter (while the central valleys bake). Decanter article here. This might make sense in Puget Sound, too: south sound, foothills, and eastern areas would heat up, pulling in cool air, and making Puget Sound that much cooler and wetter. I hope not!

So I’m keeping my fingers crossed that the rains will hold off until November, and the “month-late” pattern will persist with sun until then.

Find an R-suitable project and force yourself to use it! R is really a programming language, not a â€œstatistics packageâ€ â€¦ and like any programming language, you can only learn it by using it to accomplish something.

What makes a project R-suitable? I divide that into three groups:

1. Projects that need cutting-edge or custom statistical methods. R quite simply is the tool where new methods are developed first. If you need to try the latest in Bayesian, machine learning, classification, genomics, or similar areas: do it in R.

2. Processes that benefit from Râ€™s language and object structure. This is why I started with the S language back in 1997: I needed to run hundreds of models and extract key information from them. If you need to bootstrap a process, or compare or iterate models, R is the place.

3. Something that you know quite well. This is where R offers little attraction, but where you can leverage your knowledge. A frequency analysis you do every day; a regression model you run every month; a chart that you can make in 5 seconds in Excel â€“ those are great places to replicate the work in R just to force yourself up the learning curve.

Note that groups #1 and #2 are the easiest and luckiest places to be: if nothing else does what you want (except complete custom code), then R is an obvious answer. Group #3, choosing a problem you could solve elsewhere, is the most frustrating and requires enormous discipline. Youâ€™ll be questioning R every step of the way (â€œwhy canâ€™t I just point and click?!â€) â€¦ until something clicks and you discover the answer for yourself. OTOH, #3 is the easiest place to start from the perspective of finding specific help for your task; if it can be done easily somewhere else, then a recipe has likely been developed for R.

But again, and most importantly: Pick a problem, use R to solve it, and stay with it until youâ€™re done. Then repeat. R undoubtedly will frustrate you. It may take hours or even days for something that seems like it should be simple. Remember that youâ€™re learning a new language, so progress should be slow. Yet every time you go through the process (choose, use, stick with it) youâ€™ll know more and will work faster and better. Good luck!

I just posted my “Rcbc” code (Rcbc R scripts), which demonstrates some core functionality for choice-based conjoint analysis (CBC) in R. This code is in development, and represents a package-in-progress. [For those new to CBC, it allows one to determine user preference and tradeoffs among products or product features using a variety of logistic regression.]

The Rcbc code is primarily useful for didactic purposes to show how conjoint models work and to show a relatively easy-to-understand gradient descent method for aggregate multinomial logit model estimation (MNL). It may help supplement commercial CBC software (e.g., Sawtooth Software) for some analytic tasks such as MNL estimation on subsamples, or determining attribute importance, or for getting data and design matrices into a simple format. Note that more complete R functionality for conjoint models is provided in the “bayesm”, “clogit”, and “mlogit” packages.

I have not yet written a code vignette, but the code is reasonably well-commented and there are various executable walkthroughs presented inside “if false” blocks in the code. Note that there may be both large and small bugs!

To use: (1) save the file as a â€œ.Râ€ file. (2) source it in its entirety (warning: functions will go into global namespace). (3) read the code and try the examples.

I do a lot of travel from the US West Coast to Asia, Europe, and US East Coast. I’ve seen great results lately using Melatonin to help with jet lag. But finding exactly how to use Melatonin was not simple and advice conflicted, so I wanted to share my easy recipe. Of course this is not a medical recommendation, just my experience.

My recipe: take 0.5-1.0mg of Melatonin exactly 9 hours before the time you want to be awake (regardless of current time where you are). Go to sleep if and when you can.

Here are some scenarios to see how this works.

Sleeping within the current time zone. You’re staying in place and want to be up and awake at 7am. Subtract 9 hours: 7am-9 = 10pm. Take melatonin at 10pm and go to sleep then or later. Set an alarm for 7am. When you wake, no matter how much sleep you had, you should be better adjusted to the time.

Flying East from US to Europe (or Asia to US). Suppose you’re leaving Seattle for a 10-hour flight that arrives in France at 8am. You want to be up and awake at 8am. In this case, the time where you are is irrelevant. Subtract 10-hour-flight – 9 hours = 1 hour into the flight. Take melatonin 1 hour into your flight, even if that’s at 4pm. Sleep as much as you can. In my case, this usually means that I don’t sleep much on the flight although I am groggy — but 9 hours later I feel more awake.

Flying West from US to Asia (or Europe to US). This is the hardest one. You have a 12-hour flight from Seattle to China. It leaves at 2:00pm Seattle time and gets to China at 6:00pm the next day, +18 hours in the time zone. You want to be on a schedule to be awake at 8:00am *China* time. First, figure out what time you want to be awake on *Seattle* time. 8:00am – 18 hours = 2:00pm. Now back up 9 hours from that to get the Melatonin time: 2:00pm – 9 = 5:00am. You want to take melatonin at 5:00am Seattle time. This means to take it twice: first (optionally but ideally) at 5:00am or soon thereafter on the day you’re leaving Seattle. Then stay up when you arrive — no naps!– and take Melatonin again at 11pm China time (8am-9 hours) or as close to that as you can stay up.

It appears that melatonin works for some but not all people. There are also conflicting sets of advice out there, such as taking Melatonin just before bedtime, which is not very different from my recipe in many cases — or, more differently, to take it in the morning or afternoon. So some experimentation may be needed to see what works best for you.

Another thing to manage is how to stop taking melatonin after arriving. My schedule it to use it for 3 days, then skip a day, then take for up to 2 more days if needed. Then stop.

In any case, be sure to get plenty of light and exercise. Walk as much as possible! Get outside, especially if it’s sunny. Don’t spend all your time indoors or in meetings. Use the hotel’s fitness center. Eat lots of fresh fruit and vegetables (or cooked, if needed where you’re going). And don’t take a nap of more than 90 minutes — set an alarm. Good luck!

Just did a taste of my five 2009 wines from their carboys. Four of these were made from Eastern Washington grapes (Cab, Merlot, Sangiovese, Syrah), while the fifth is a mix of my own grapes (Regent, Dolcetto) plus some of the Cab & Merlot.

The best two now are clearly the Merlot and the Regent blend. The Merlot has big fruit, moderate acidity, and is a little on the hot side. The Regent blend is the darkest of all the wines — thanks to extended maceration — and has a complex Bordeaux profile with more tannin than any of the others (even the Cab). It could very well be the best of all the wines in the end. If so, it will be a testament to the extremely closely tended vineyard (because it’s in my yard and I could tend it almost daily). Every Regent and Dolcetto grape that went into that wine was an absolutely perfect berry.

The Cab has a good deep flavor but is not very complex. The Sangiovese is fruity but a bit thin and somewhat acidic. It is very light in color. I might go light on the wood with it and make it a chillable rose’ alternative. The Syrah is still a problem: it has good fruit and balance, plus a very distinctive Syrah pepper taste and a long finish. But it has lingering problems on the bouquet that I just can’t get rid of, a bit of faint H2S. the Syrah grapes were definitely in the worst shape when I received them and it’s showing up.

I’ll try the Sangiovese again in a month or two and then decide whether to go ahead and bottle that. The others get at least another six months and probably twelve months of aging.

I posted earlier about my new favorite laptop bag. Now I’d like to mention the laptop sleeve that I use with it.

I wanted a sleeve to add some protection and have an alternative to carry it when I don’t need the whole bag. I found SF Bags and was delighted with their service. I specified my laptop model and the kind of sleeve I wanted. There was great follow-up from them and the bag shipped quickly. But what I didn’t expect was the fabulous quality — very nicely made and a perfect fit for my laptop. And it was made in the US. I’d order again in a heartbeat.

This has my vines getting ready to bud. Usually that means it would be time to prune … but I’m worried it could still freeze in February or March. Last year I estimate I lost at least 1/2 of the crop to a hard freeze. So I’m going to hold off on pruning until early March if possible. Leaving the vines unpruned will delay growth in the close-in buds and hopefully protect against the chance of a freeze.

It’s exciting to think that bud break will probably be 4-6 weeks earlier than last year. If the summer is consistent and long we could have a very nice crop.